Machine Learning-Based Media Content Placement

ABSTRACT

A system performs an automated analysis on a set of related media content items, such as static and video display advertisements for a coordinated advertising campaign. The analysis can include, for example, recognition of products, services, brands, objects, music, speech, motion, colors and moods, in order to determine content profiles for the content items. Different sequences of the media content items are placed within the web browsing paths of individual users, and the responses to the sequences are monitored with respect to desired outcomes, such as the purchase of a product or the visiting of an advertiser&#39;s website. The content profiles, the sequences, the placements, and the responses are provided as input into a machine learning system that is trained to select sequences and placements of media content items that achieve the desired outcomes. The system can be trained in part or wholly using feedback from its own output.

RELATED APPLICATIONS

The subject matter of this application is related to U.S. Utility patentapplication Ser. No. 16/022,161, now U.S. Pat. No. 11,270,337, filed on2018 Jun., 28, U.S. Provisional Application No. 62/583,439, filed on2017 Nov., 8, U.S. Provisional Application No. 62/612,604, filed on 2017Dec., 31, and U.S. Provisional Application No. 62/691,337, filed on 2018Jun., 28, all of which applications are incorporated herein by referencein their entireties.

BACKGROUND OF THE INVENTION

Current trends in web and app targeted advertising placement focusprimarily on placing individual advertisements. A sequence of relatedadvertisement placements, however, placed within or on the pages of asingle user's web browsing path or clickstream may be able to achieve anadvertising objective that may not be able to be achieved through anyone single advertisement. The sequence of placements, may, for example,appear at various times and at different or unrelated locations on theWorld Wide Web for any one user. Known techniques can be used to track asingle user and target a sequence of advertisements to a user, forexample, to convey to the user an evolving story about a product or abrand. What is needed, however, is a way to automate the sequencing andplacement of creative content to individual users with a goal ofimproving effectiveness of the sequence while reducing or eliminatingthe need for manual sequencing and placement.

SUMMARY OF THE INVENTION

A system performs an automated analysis on a set of related mediacontent items, such as static and video display advertisements for acoordinated advertising campaign. The analysis can include, for example,recognition of products, services, brands, objects, music, speech,motion, colors and moods, in order to determine content profiles for thecontent items. Different sequences of the media content items are placedwithin the web browsing paths of individual users, and the responses tothe sequences are monitored with respect to desired outcomes, such asthe purchase of a product or the visiting of an advertiser's website.The content profiles, the sequences, the placements, and the responsesare provided as input into a machine learning system that is trained toselect sequences and placements of media content items that achieve thedesired outcomes. The system can be trained in part or wholly usingfeedback from its own output.

A method is performed by a computer system having at least one processorand a memory. The method can include: for each media content item of aplurality of related media content items, generating a content profilefor the media content item by performing an automated analysis of themedia content item, wherein the content profile comprises a plurality ofattribute fields each of which attribute fields specifies acharacteristic of the media content item determined based on theautomated analysis; for each sequence of a plurality of differentsequences of media content items selected from the plurality of relatedmedia content items, and with respect to the each sequence, for eachuser of a plurality of users: presenting an instance of the sequence by,for each media content item in the sequence, causing an instance of theeach media content item to be presented to the each user, and for eachinstance in which a media content item in the sequence is presented tothe each user, associating the each instance in which the media contentitem is presented with: the instance of the sequence, and an interactionprofile indicative of user responses related to the presentation of themedia content item; selecting a target media content item from theplurality of related media content items to present to a target userbased on: a sequence of the plurality of related media content itemspreviously presented to the target user, the content profiles for theplurality of related media content items, the interaction profiles forthe instances in which media content items are presented, and theassociations between instances of media content items being presentedand instances of sequences in which media content items are presented;and causing the target media content item to be presented to the targetuser.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a timeline of user interactions with a video segmentbeing presented to a user.

FIG. 2 illustrates a functional diagram of a system formachine-learning-based video segment placement in accordance with oneembodiment.

FIG. 3 illustrates a method performed by the system in one embodimentfor targeting individual video segments to individual users based on acontent profile and user interaction profiles.

FIG. 4 illustrates a method performed by the system in one embodimentfor establishing a target placement profile for a target video segmentbased on a content profile and user interaction profiles.

FIG. 5 illustrates a functional block diagram of a system formachine-learning-based media content item sequencing and placement inaccordance with one embodiment.

FIG. 6 illustrates a method for sequencing and placing media contentitems for presentation to target users.

FIG. 7 illustrates a method for selecting a media content items forpresentation to a target user.

FIG. 8 illustrates a general computer architecture that can beappropriately configured to implement components disclosed in accordancewith various embodiments.

DETAILED DESCRIPTION

In the following description, references are made to various embodimentsin accordance with which the disclosed subject matter can be practiced.Some embodiments may be described using the expressions one/an/anotherembodiment or the like, multiple instances of which do not necessarilyrefer to the same embodiment. Particular features, structures orcharacteristics associated with such instances can be combined in anysuitable manner in various embodiments unless otherwise noted.

FIG. 1 illustrates a timeline 100 of user interactions with a videosegment being presented to a user. FIG. 1 contemplates that the video isbeing viewed by the user using a user computing device, such as adesktop computer, tablet or mobile phone, which is capable of receivinguser input and/or of monitoring the user's actions, such as using avideo camera.

At a first point in time, the video segment advertising a product isstarted, which might occur, for example in response to the loading of aweb page selected by the user. A system (which can include the usercomputing device and/or a cloud-computing based system) determines thatthe user next viewed the video for 5 seconds, looked at the bottom leftcorner of the video or the page, and then looked away. Thesedeterminations can be made by analysis of a desktop computer webcaminput or by front facing video camera input from a mobile phone ortablet. The system then determines that the video became obscured byanother window on a user interface of the user computing device. Next,the video ends and a survey is shown to the user, in response to whichthe user takes the survey and rates the video highly. Next, possibly atsome substantially later time and possibly in an unrelated web browsingsession, the user makes an online purchase for the advertised product.Next, the system determines that the user makes an in-store purchase ofthe advertised product, such as by associating a loyalty program used bythe user with the user's online identity (other known techniques couldbe used).

In different configurations, different types of user computing devicesmight be configured to gather different types of input. For example, theuser's device could be a smart television that is only able to determinewhether videos or display advertisements were displayed in part or intheir entirety. The smart television could, however, be outfitted with aviewer facing video camera to determine, numbers of viewers, viewerglances and/or facial expressions. A tablet, mobile phone or personalcomputer could be configured to gather voice, video, and various typesof click, touch and keyboard input.

FIG. 2 illustrates a functional diagram of a system 200 formachine-learning-based video segment placement in accordance with oneembodiment. FIG. 3 illustrates a method 300 performed by the system 200in one embodiment for targeting individual video segments to individualusers based on a content profile and user interaction profiles. FIG. 4illustrates a method 400 performed by the system 200 in one embodimentfor establishing a target placement profile for a target video segmentbased on a content profile and user interaction profiles. The methods300 and 400 will now be discussed with reference to the functionaldiagram of FIG. 2.

Although FIGS. 2-4 are discussed below with reference to video segments,it should be understood that the Figures and the associated methods canbe used with respect to any type or combination of types of mediacontent items, which can include, for example, video segments, displayadvertisements, images, audio segments, and documents, such as webpages. Other types of media, such as virtual reality experiences, canalso be included and the methods disclosed herein are not necessarilylimited to any particular type of media content.

FIG. 3 illustrates a method 300 for targeting individual video segmentsto individual users based on a content profile and previously collecteduser interaction profiles. At a first step 304, the system 200 iteratesover each video segment of a plurality of video segments 204 (FIG. 2).The plurality of video segments may be related, such as all being partof a coordinated advertising campaign, or the video segments might notnecessarily be related, such as being completely unrelated videosegments or advertisements. In the case of advertisements, the objectiveof the system might be to produce a purchase action by a user or toeducate users about a brand. The video segments might not beadvertisements, and may, for example, be video segments of a free orpaid video service where an objective of the system might be to createan enjoyable viewing experience for the user.

The term “iterate” is used herein for the purpose of convenience, and itshould be understood to include performing certain steps for each itemin a collection. The term iterate should not be understood to imply orrequire a specific iterative looping construct or order. Unlessindicated otherwise, the system may perform the associated steps withinthe iteration and for each item in any manner or order, such as using aspecific loop, in parallel, or asynchronously and potentially at thecontrol of another process, processes, or individuals, such as users.

At a step 308, for each video segment, the system can generate a contentprofile 210 for the video segment by performing an automated analysis208 of the video segment. The content profile can include a plurality ofattribute fields where each attribute field specifies a characteristicof the video segment determined based on the automated analysis. Theattribute fields can include fields to store, for example:identifications of objects depicted in the video segment and identifiedby the automated analysis, identifications of music contained in audioof the video segment and identified by the automated analysis,identifications of spoken words contained in audio of the video segmentand identified by the automated analysis, identifications of motiondepicted in the video segment and identified by the automated analysis,or a category (e.g. selected from a taxonomy of products and/orservices) assigned to the video segment by the automated analysis. Inone embodiment, the video segment can include metadata relating to thevideo segment and the automated analysis of the video segment caninclude analyzing the metadata.

At a step 312, for each video segment, the system iterates over eachuser of a plurality of users to which the video segment is presented.The system itself may present the video segment or it may cause thevideo segment to be presented to each user, such as through anadvertising network. Each different video segment of the step 304 may beshown to a different plurality of users or the same plurality of users.

At a step 316, for each user, the system can generate an interactionprofile 218 for the user with respect to the video segment based on userinput to a computer in association with a presentation 216 of the videosegment to the user. The presentation of the video segment to the usercan performed by a separate device from the system 200, such as anycombination of a content delivery network, advertising network, or otherdistribution network in conjunction with a user-operated device, such asa personal computer, tablet, mobile phone, smart television, or set-topbox.

The user input can include active user input such as, for example:keyboard input, pointer selection, button press, voice capture, andtouch screen selection. The user input can include passive user inputsuch as, for example: visibility of the presentation of the videosegment on a computer display, absence of user input interrupting thepresentation of the video segment, an indication that the presentationof the video segment continued to completion, and use of a camera todetermine whether the user is viewing the presentation of the videosegment. The visibility of the video segment on a computer display canbe determined based on whether another application window or scrollingof a page partly or fully obscures display of the video segment.

In one embodiment, the interaction profile can include one or moreinteraction fields, which can indicate, for example: whether the videosegment was presented to the user in its entirety, how much of the videosegment was presented to the user, or a time location within the videosegment at which the user selected a selectable link presented in orwith the video segment. The interaction profile can include anindication of intent by the user to take an action, which, for example,can be obtained in response to a survey presented to the user inassociation with the presentation of the video segment to the user.

In one embodiment, the system can also generate an interaction scorebased on the interaction profile and/or calculate an interaction scoreto be included in the interaction profile. The interaction score can be,for example, a score representing a level of engagement or interactionby the user with the presentation. In one embodiment, the contentprofile for the video segment can include an inflection point specifyinga time location within the video segment at which an event identified bythe automated analysis occurs, and the interaction profile can includean input time location within the video segment at which the userprovided input. The interaction score can be further based on acomparison between the inflection point and the input time.

At a step 320, which can occur after the iterations of step 304, thesystem can select a target plurality of users 220 for a target videosegment 204A of the plurality of video segments 204. For example, thesystem can be in the process of implementing an advertising campaign andit may need to display a certain target video advertising segment acertain number of additional times to a certain number of target users.The system can select the plurality of target users based on: thecontent profile 210A for the target video segment as well as data forone or more subject video segments selected from the plurality of videosegments. The data from the subject video segments can include, forexample, the content profile for the subject video segment and theplurality of interaction profiles for users with respect to the subjectvideo segment. The interaction profiles can include or be an interactionscore.

In the step 320, it may be the case that the system leverages data fromonly a subset of the plurality of video segments, or it may make theselection based on data for all of the plurality of video segments. Forexample, the system can input the content profiles of the step 308 andthe interaction profiles of the step 316 as training data into a machinelearning system 222, such as a neural network or a Bayesian network,along with indications of the desired outcomes in terms of favorableinteraction profiles. The trained machine learning system can then makedeterminations of which target users might be likely to produce thefavorable or desired interaction profiles. In one embodiment, the targetvideo segment may have already been presented to some users, in whichcase feedback from those presentations can be leveraged in selecting thetarget plurality of users.

In one embodiment, a user profile can be generated for each user of aplurality of users, and the selecting a target plurality of users can befurther based on the user profiles. The user profile can include, forexample, one or more of: location of the user (e.g. residence location,residence country), geolocation of the user (e.g. specific currentgeographic location), age of the user, gender of the user, andpersonality of the user. The user profile can include preferencesinferred based on prior actions of the user, such as prior purchases,browsing history, prior interaction profiles, and advertising profiles.

In one embodiment, the selecting a target plurality of users can includedetermining an engagement likelihood score. The engagement likelihoodscore can be based on the content profile for the target video segment.The engagement likelihood score can also or alternatively be based on,for each of the at least one subject video segment selected from theplurality of video segments, the content profile for the subject videosegment and the plurality of interaction profiles for users with respectto the subject video segment. The target plurality of users can beselected based on the engagement likelihood score.

At a step 324, the system causes the target video segment to bepresented to at least one of the selected target plurality of users. Forexample, once the target plurality of users is selected, the system canconfigure the target segment to be displayed to one or more of thetarget plurality of users through an advertising network that tracks andtargets individual users. On the other hand, the system can beconfigured to directly control the display of the target video to users,for example, in the case where the system supports a subscription ornon-subscription video viewing service for the users. In this case, thesystem itself can be configured to directly display the target video tothe target users as the users request a next video to watch or whenthere is an opportunity to automatically show a video to a user.

FIG. 4 illustrates a method 400 for establishing a target placementprofile for a target video segment based on a content profile and userinteraction profiles. The methods 400 can be performed by a singlesystem together or separately and accordingly any description, featuresor functionality described above with respect to the method 300, but notrepeated below, should be understood to also apply to the method 400 asapplicable.

At a first step 404 the system 200 iterates over each video segment of aplurality of video segments 204 (FIG. 2). The plurality of videosegments may be related, such as all being part of a coordinatedadvertising campaign, or the video segments might not necessarily berelated, such as being completely unrelated video segments oradvertisements. In the case of advertisements, the objective of thesystem might be to produce a purchase action by a user or to educateusers about a brand. The video segments might not be advertisements, andmay, for example, be video segments of a free or paid video servicewhere an objective of the system might be to create an enjoyable viewingexperience for the user.

The term “iterate” is used herein for the purpose of convenience, and itshould be understood to include performing certain steps for each itemin a collection. The term iterate should not be understood to imply orrequire a specific iterative looping construct or order. Unlessindicated otherwise, the system may perform the associated steps withinthe iteration and for each item in any manner or order, such as using aspecific loop, in parallel, or asynchronously and potentially at thecontrol of another process, processes, or individuals, such as users.

At a step 408, similar to the step 308, for each video segment, thesystem can generate a content profile 210 for the video segment byperforming an automated analysis 208 of the video segment. The contentprofile can include a plurality of attribute fields where each attributefields specifies a characteristic of the video segment determined basedon the automated analysis. The attribute fields can include fields tostore, for example: identifications of objects depicted in the videosegment and identified by the automated analysis, identifications ofmusic contained in audio of the video segment and identified by theautomated analysis, identifications of spoken words contained in audioof the video segment and identified by the automated analysis,identifications of motion depicted in the video segment and identifiedby the automated analysis, or a category (e.g. selected from a taxonomyof products and/or services) assigned to the video segment by theautomated analysis. In one embodiment, the video segment can includemetadata relating to the video segment and the automated analysis of thevideo segment can include analyzing the metadata.

At a step 412, for each video segment, the system iterates over eachinstance in which the segment is presented to a user. The system itselfmay present the video segment or it may cause the video segment to bepresented to each user, such as through an advertising network. Eachdifferent video segment of the step 404 may be shown to a differentplurality of users or the same plurality of users.

At a step 416, for each instance in which the video segment ispresented, the system associates the instance with a placement profile242 (FIG. 2), descriptive of the placement of the video segment. Theplacement profile can include any one or more of: geolocation ofpresentation, time of presentation, monitored weather at geolocation ofpresentation, hosting web page uniform resource locator, identifier ofhosting web page, domain of hosting web page, viewing user identity,viewing user profile, viewing device type, sematic category of hostingweb page, sentiment of hosting web page, device type, browser type, andidentifier of presenting application.

A viewing user profile can include, for example, one or more of:location of the user (e.g. residence location, residence country),geolocation of the user (e.g. specific current geographic location), ageof the user, gender of the user, and personality of the user. The userprofile can include preferences inferred based on prior actions of theuser, such as prior purchases, browsing history, prior interactionprofiles, and advertising profiles.

At a step 418, for each instance, the system can generate and associatethe instance with an interaction profile 218 for the instance withrespect to the video segment based on user input to a computer inassociation with a presentation 216 of the video segment to the user.The presentation of the video segment to the user can performed by aseparate device from the system 200, such as any combination of acontent delivery network, advertising network, or other distributionnetwork in conjunction with a user-operated device, such as a personalcomputer, tablet, mobile phone, smart television, or set-top box.

The user input can include active user input such as, for example:keyboard input, pointer selection, button press, voice capture, andtouch screen selection. The user input can include passive user inputsuch as, for example: visibility of the presentation of the videosegment on a computer display, absence of user input interrupting thepresentation of the video segment, an indication that the presentationof the video segment continued to completion, and use of a camera todetermine whether the user is viewing the presentation of the videosegment. The visibility of the video segment on a computer display canbe determined based on whether another application window or scrollingof a page partly or fully obscures display of the video segment.

In one embodiment, the interaction profile can include one or moreinteraction fields, which can indicate, for example: whether the videosegment was presented to the user in its entirety, how much of the videosegment was presented to the user, or a time location within the videosegment at which the user selected a selectable link presented in orwith the video segment. The interaction profile can include anindication of intent by the user to take an action, which, for example,can be obtained in response to a survey presented to the user inassociation with the presentation of the video segment to the user.

In one embodiment, the system can generate an interaction score based onthe interaction profile and/or calculate an interaction score to beincluded in the interaction profile. The interaction score can be, forexample, a score representing a level of engagement or interaction bythe user with the presentation. In one embodiment, the content profilefor the video segment can include an inflection point specifying a timelocation within the video segment at which an event identified by theautomated analysis occurs, and the interaction profile can include aninput time location within the video segment at which the user providedinput. The interaction score can be further based on a comparisonbetween the inflection point and the input time.

In one embodiment, a single interaction profile can be associated with aplurality of instances in which the video segment is presented based onaggregated user response data associated with the plurality ofinstances. The aggregated user response data can be collected based onuser actions performed subsequent to the plurality of instances in whichthe video segment is presented. For example, the aggregated userresponse data can be based on product purchase data collected inassociation with one or more of: a geographic area; a temporal duration;and viewing user geolocation tracking, correlated with the plurality ofinstances in which the video segment is presented. The product purchasedata can be indicative of one or more of: online product purchases andin-person product purchases.

At a step 420, which can occur after the iterations of step 404, thesystem can establish a target placement profile 242A (FIG. 2) for atarget video segment 204A of the plurality of video segments 204. Forexample, the system can be in the process of implementing an advertisingcampaign and it may need to display a certain target video advertisingsegment a certain number of additional times to a certain number ofusers. The system can select the target placement profile for presentingthe target video segment to users based on the content profile 210A forthe target video segment as well as data for the plurality of videosegments. The data for the plurality of video segments can include thecontent profile for each video segment. The data can also include, foreach instance in which each video segment has been presented: theassociated placement profile and the associated interaction profile. Theassociated interaction profile can include an interaction score.

The target placement profile can be established by, for example,creating a new profile, selecting a profile from a set of predeterminedor preexisting profiles, and/or by combining features or characteristicsof previously detected placement profiles. The target placement profilecan include any one or more of the features noted above with respect tothe placement profile.

The target video may or may not be one of the plurality of segments.Accordingly, the target video segment may have already been presented tosome users using some prior placements, in which case feedback fromthose presentations can be leveraged in selecting the target pluralityof users. In this case, the establishing a target placement profile canbe further based on, for each instance of a plurality of instances inwhich the target video segment is presented: the associated placementprofile, and the associated interaction profile.

In the step 420, it may be the case that the system leverages data fromonly a subset of the plurality of instances in which video segments arepresented to users, or it may make the selection based on data for allof the instances. For example, the system can input the content profilesof the step 408 , the placement profiles of the step 416, and theinteraction profiles of the step 418 as training data into the machinelearning system 222, such as a neural network or a Bayesian network,along with indications of the desired outcomes in terms of favorableinteraction profiles. The trained machine learning system can then makedeterminations of which target placement profiles might be likely toproduce the favorable or desired interaction profiles. In oneembodiment, the target placement profile may have already been used inpractice, in which case feedback from the associated presentations canbe leveraged in selecting the target placement profile.

At a step 424, the system causes the target video segment to bepresented in accordance with the established or selected targetplacement profile. For example, once the target profile is established,the system can configure the target segment to be displayed to users inone or more placements matching the target placement through anadvertising network offering targeted advertisement placements.

FIG. 5 illustrates a functional block diagram of a system 500 formachine-learning-based media content item sequencing and placement inaccordance with one embodiment. FIG. 6 illustrates a first method 600and FIG. 7 illustrates a second method for sequencing and placing mediacontent items for presentation to target users. The methods 600 and 700will now be discussed with reference to the functional blocks of FIG. 5.The system 500 can be integrated with the system 200 and the methods 600and 700 can be performed by the system 500 and/or 200 in conjunctionwith and/or integrated with the methods 300 and/or 400. Accordingly, anydescription, features or functionality described above with respect tothe methods 300 and 400, but not repeated below, should be understood toalso apply to the methods 600 and 700 as applicable. Similarly,description, features or functionality described below with respect toeither of the methods 600 or 700 should be understood to also apply tothe others of methods 300, 400, 600 and 700 as applicable. The methods600 and 700, like the methods 300 and 400 can be used with respect toany type or combination of types of media content items, which caninclude, for example, video segments, display advertisements, images,audio segments, and documents, such as web pages. Other types of media,such as virtual reality experiences, can also be included and themethods disclosed herein are not necessarily limited to any particulartype of media content.

FIG. 6 illustrates a method 600 for sequencing and placing media contentitems for presentation to target users. At a first step 604, the system500 iterates over each media content item of a plurality or asset poolof media content items 504 (FIG. 5). The plurality of media contentitems may be related, such as all being part of a coordinatedadvertising campaign, or the items might not necessarily be related,such as being completely unrelated video segments or advertisements. Inthe case of advertisements, the objective of the system might be toproduce a purchase action by a user or to educate users about a brand.The media content items might not be advertisements, and may, forexample, be web pages.

The term “iterate” is used herein for the purpose of convenience, and itshould be understood to include performing certain steps for each itemin a collection. The term iterate should not be understood to imply orrequire a specific iterative looping construct or order. Unlessindicated otherwise, the system may perform the associated steps withinthe iteration and for each item in any manner or order, such as using aspecific loop, in parallel, or asynchronously and potentially at thecontrol of another process, processes, or individuals, such as users.

At a step 608, for each media content item, the system can generate acontent profile 510 for the media content item by performing anautomated analysis 208 of the media content item. The content profilecan include a plurality of attribute fields where each attribute fieldspecifies a characteristic of the media content item determined based onthe automated analysis. The attribute fields can include fields tostore, for example: identifications of objects depicted in the mediacontent item and identified by the automated analysis, identificationsof music contained in audio of the media content item and identified bythe automated analysis, identifications of spoken words contained inaudio of the media content item and identified by the automatedanalysis, identifications of motion depicted in the media content itemand identified by the automated analysis, or a category (e.g. selectedfrom a taxonomy of products and/or services) assigned to the mediacontent item by the automated analysis. In one embodiment, the mediacontent item can include metadata relating to the media content item andthe automated analysis of the media content item can include analyzingthe metadata.

At a step 612, the system first iterates over different sequences ofcontent items to be presented to users. The different sequences can beinitially selected randomly, and eventually after training of thesystem, the system can focus in on sequences that perform better.Accordingly, the system iterates over each sequence of a plurality ofdifferent sequences of media content items selected from the pluralityof media content items. At a step 614, for each sequence of contentitems, the system iterates over each user of a plurality of users. Theplurality of users for each iteration for each sequence may be adifferent plurality of users or the same plurality of users.

At a step 616, for the each user and for each sequence, the systemcauses an instance of each media content item in the each sequence to bepresented to the each user. At a step 618, for the each instance inwhich a media content item in the each sequence is presented to the eachuser, the system associates the each instance in which the media contentitem is presented with: the instance of the each sequence and aninteraction profile 218 or interaction data indicative of user responsesrelated to the presentation of the media content item. The steps 616 and618 can be performed for various combinations of the iterations over thesteps 612 and 614 after which control can pass to a next step 620.

As discussed above with respect to the methods 300 and 400, theinteraction profile can include one or more interaction fields, whichcan indicate, for example: whether the media content item was presentedto the user in its entirety, how much of the media content item waspresented to the user, or a time location within the media content itemat which the user selected a selectable link presented in or with themedia content item. The interaction profile can include an indication ofintent by the user to take an action, which, for example, can beobtained in response to a survey presented to the user in associationwith the presentation of the media content item to the user.

At the step 620, a target media content item is selected from theplurality of media content items to present to a target user based on: asequence of media content items previously presented to the target user,the content profiles for the plurality of media content items, theinteraction profiles for the instances in which media content items arepresented, and the associations between instances of media content itemsbeing presented and instances of sequences in which media content itemsare presented.

In the step 620, it may be the case that the system leverages data fromonly a subset of the plurality of sequences and/or media content items,or it may make the selection based on all available data. For example,the system 500 can input the content profiles of the step 608, and theassociations and the interaction profiles of the step 618 as trainingdata into a machine learning system 522, such as a neural network or aBayesian network, along with indications of the desired outcomes interms of favorable interaction profiles (e.g. training data 524 in FIG.5). The trained machine learning system can then make determinations ofwhich target users might be likely to produce the favorable or desiredinteraction profiles. In one embodiment, the sequence of media contentitems previously presented to the target user may have already beenpresented to other users, in which case feedback from thosepresentations can be leveraged in selecting the target media contentitem.

At a step 624, the system causes the target media content item to bepresented to the target user. For example, once the target media contentitem is selected, the system can configure the target media content itemto be displayed to the target user through an advertising network thattracks and targets individual users. For example, the media content itemcan be presented to the user through an advertising placement such as atargeted web advertising placement or a targeted mobile app advertisingplacement.

On the other hand, the system can be configured to directly control thedisplay of the target item to the user, for example, in the case wherethe system supports a subscription or non-subscription content deliveryservice or website for the user. In this case, the system itself can beconfigured to directly display the target item to the target user as theuser requests a next item.

In one embodiment, for the each instance in which a media content itemin the each sequence is presented to the each user, the each instancecan be further associated with a placement profile descriptive of acontext in which the media content item is presented. In this case, atarget placement profile can be established for the target media contentitem based on: the placement profiles for the instances in which mediacontent items are presented, and the interaction profiles for theinstances in which media content items are presented. The target mediacontent item can then be presented to the target user in accordance withthe target placement profile. The establishing a target placementprofile for the target media content item can be further based on thecontent profiles for the plurality of media content items. Theestablishing a target placement profile for the target media contentitem can be further based on the sequence of media content itemspreviously presented to the target user. The placement profile caninclude, for example, any one or more of: geolocation of presentation,time of presentation, monitored weather at geolocation of presentation,hosting web page uniform resource locator, identifier of hosting webpage, domain of hosting web page, viewing user identity, viewing userprofile 560, viewing device type, sematic category of hosting web page,sentiment of hosting web page, device type, browser type, and identifierof presenting application.

FIG. 7 illustrates a method 700 for selecting a media content items forpresentation to a target user. At a first step 704, the system 500iterates over each sequence of a plurality of different sequences ofmedia content items, wherein the media content items are selected from aplurality of media content items in order to collect media content itemsequence response data. The plurality of media content items may berelated, such as all being part of a coordinated advertising campaign,or the items might not necessarily be related, such as being completelyunrelated video segments or advertisements. In the case ofadvertisements, the objective of the system might be to produce apurchase action by a user or to educate users about a brand. The mediacontent items might not be advertisements, and may, for example, be webpages.

The term “iterate” is used herein for the purpose of convenience, and itshould be understood to include performing certain steps for each itemin a collection. The term iterate should not be understood to imply orrequire a specific iterative looping construct or order. Unlessindicated otherwise, the system may perform the associated steps withinthe iteration and for each item in any manner or order, such as using aspecific loop, in parallel, or asynchronously and potentially at thecontrol of another process, processes, or individuals, such as users.

At a step 708, for each sequence in turn, the system iterates over eachuser of a plurality of users. At a step 712, each sequence is presentedto the plurality of users. The system itself may present the videosequence or it may cause the sequence to be presented to each user, suchas through an advertising network. Each different sequence may be shownto a different plurality of users or the same plurality of users. At astep 716, the system receives and/or collects user response dataindicative of user responses related to the presenting the eachsequence.

At a step 720, a target media content item is selected from theplurality of media content items to present to a target user based on: asequence of media content items previously presented to the target userand the collected media content item sequence response data. At a step724, the system causes the target media content item to be presented tothe target user.

The sequence of media content items previously presented to the targetuser can include a portion of one of the plurality of differentsequences of media content items. The portion can be an initial portionof the one of the plurality of different sequences of media contentitems including at least a first media content item from the one of theplurality of different sequences.

The selecting a target media content item can be performed using atrained machine learning system. The trained machine learning system canbe trained using the collected media content item sequence responsedata. The trained machine learning system can be a neural network. Thetrained machine learning system can be a Bayesian network. The mediacontent item sequences can include video segments and non-video displayadvertisements. The media content item sequences can include userconsumable content including, for example, video segments, displayadvertisements, images, audio segments, and documents.

The method 700 can further include: for each media content item of theplurality of media content items: generating a content profile for themedia content item by performing an automated analysis of the mediacontent item, wherein the content profile for the media content itemcomprises a plurality of attribute fields, each of which attributefields specifies a characteristic of the media content item determinedbased on the automated analysis of the media content item, and for eachinstance of a plurality of instances in which the media content item ispresented, associating the each instance with: a placement profiledescriptive of a context in which the media content item is presented,and an interaction profile indicative of user responses related to thepresentation of the media content item; and for the target media contentitem, establishing a target placement profile for the target mediacontent item based on: a content profile for the target media contentitem generated by performing an automated analysis of the target mediacontent item, wherein the content profile for the target media contentitem comprises a plurality of attribute fields each of which attributefields specifies a characteristic of the target media content itemdetermined based on the automated analysis of the target media contentitem, the content profiles for the plurality of media content items, theinteraction profiles for the instances in which media content items arepresented, and the placement profiles for the instances in which mediacontent items are presented, wherein the target media content item ispresented to the target user in accordance with the target placementprofile.

The target media content item can be selected from the plurality ofmedia content items, and the establishing a placement profile for thetarget media content item can be further based on, for each instance ofa plurality of instances in which the target media content item ispresented: the associated placement profile, and the associatedinteraction profile.

A placement profile can include any combination of one or more itemssuch as: geolocation of presentation, time of presentation, monitoredweather at geolocation of presentation, hosting web page uniformresource locator, identifier of hosting web page, domain of hosting webpage, viewing user identity, viewing user profile, viewing device type,sematic category of hosting web page, sentiment of hosting web page,device type, browser type, and identifier of presenting application.Establishing a target placement profile can include creating a placementprofile. Establishing a target placement profile can include selecting aplacement profile from a plurality of preexisting placement profiles. Aplacement profile can include a viewing user identity. A placementprofile can include a viewing user profile. The viewing user profile caninclude for example, one or more of: user geolocation, user age, usergender, and user personality. The viewing user profile can include anadvertising profile including preferences inferred based on prioractions by the viewing user. A placement profile can be a viewing userprofile. A placement profile can be a viewing user identity.

Each interaction profile can include an interaction score indicative ofengagement of a viewing user with the media content item. The targetplacement profile can be established based on the interaction scores.The media content item can include a video segment, and the contentprofile for the media content item can include an inflection pointspecifying a time location within the video segment at which an eventidentified by the automated analysis occurs, and the interaction profilecan include an input time location within the video segment at which theuser provided input. The interaction score can be further based on acomparison between the inflection point and the input time.

A single interaction profile can be associated with multiple instancesin which the media content item is presented, and the single interactionprofile can be based on aggregated user response data associated withthe multiple instances. The aggregated user response data can becollected based on user actions performed subsequent to the multipleinstances in which the media content item is presented. The aggregateduser response data can be based on product purchase data collected inassociation with one or more of a geographic area, a temporal duration,and viewing user geolocation tracking correlated with the multipleinstances in which the media content item is presented. The productpurchase data can be indicative of one or more of online productpurchases and in-person product purchases.

The user response data can be based on active user input such as, forexample: keyboard input, pointer selection, button press, voice capture,and/or touch screen selection.

The media content item can include a video segment, and the userresponse data can be based on passive user input such as, for example:visibility of the presentation of the video segment on a computerdisplay, absence of user input interrupting the presentation of thevideo segment, an indication that the presentation of the video segmentcontinued to completion, and/or use of a camera to determine whether theuser is viewing the presentation of the video segment. Visibility of thevideo segment on a computer display can be determined based on whetheranother application window or scrolling of a page partly or fullyobscures display of the video segment.

The media content item can include metadata relating to the mediacontent item and the automated analysis of the media content item caninclude analyzing the metadata. The presentation of the media contentitem can be performed by a separate device from the computer system.

The media content item can include a video segment, and the interactionprofile can include at least one interaction field. One of the at leastone interaction fields can indicate whether the video segment waspresented in its entirety. One of the at least one interaction fieldscan indicate how much of the video segment was presented. One of the atleast one interaction fields can indicate a time location within thevideo segment at which time location a user selected a selectable linkpresented in association the video segment. One of the at least oneattribute fields can be configured to store identifications of objectsidentified in the media content item by the automated analysis. One ofthe at least one attribute fields can be configured to storeidentifications of music identified in the media content item by theautomated analysis. One of the at least one attribute fields can beconfigured to store identifications of spoken words identified in themedia content item by the automated analysis. One of the at least oneattribute fields can be configured to store identifications of motionidentified in the media content item by the automated analysis. One ofthe at least one attribute fields can be configured to store a categoryassigned to the media content item by the automated analysis. Thecategory can be selected from a taxonomy of products and services.

The target media content item can be, but need not be, one of theplurality of media content items.

The interaction profile can include an indication of intent by a user totake an action. The indication of intent by the user to take an actioncan be obtained in response to a survey presented to the user inassociation with the presentation of the media content item to the user.

The establishing a target placement profile can be performed using atrained machine learning system. The trained machine learning system canbe trained using the content profiles, the placement profiles, and theinteraction profiles, for the plurality of media content items. Thetrained machine learning system can be a neural network. The trainedmachine learning system can be a Bayesian network. The selecting atarget media content item can be performed using a trained machinelearning system. The trained machine learning system can be trainedusing the collected media content item sequence response data. Thetrained machine learning system can be a neural network. The trainedmachine learning system is a Bayesian network.

For each user of the plurality of users, the received user response dataindicative of user responses related to the presenting the each sequencecan be based on, for at least one media content item in the sequence, atleast one user response related to an instance in which the mediacontent item is presented to the each user.

The causing the each sequence to be presented to a plurality of userscan include, for each user of the plurality of users, causing each mediacontent item in the sequence to be presented to the each user through anadvertising placement.

The advertising placement can be a targeted web advertising placement.The advertising placement can be a targeted mobile app advertisingplacement. The plurality of media content items can all share a commonrelationship.

Computer Implementation

Components of the embodiments disclosed herein, which may be referred toas methods, processes, applications, programs, modules, engines,functions or the like, can be implemented by configuring one or morecomputers or computer systems using special purpose software embodied asinstructions on a non-transitory computer readable medium. The one ormore computers or computer systems can be or include standalone, clientand/or server computers, which can be optionally networked through wiredand/or wireless networks as a networked computer system.

FIG. 8 illustrates a general computer architecture 800 that can beappropriately configured to implement components disclosed in accordancewith various embodiments. The computing architecture 800 can includevarious common computing elements, such as a computer 801, a network818, and one or more remote computers 830. The embodiments disclosedherein, however, are not limited to implementation by the generalcomputing architecture 800.

Referring to FIG. 8, the computer 801 can be any of a variety of generalpurpose computers such as, for example, a server, a desktop computer, alaptop computer, a tablet computer or a mobile computing device. Thecomputer 801 can include a processing unit 802, a system memory 804 anda system bus 806.

The processing unit 802 can be any of various commercially availablecomputer processors that can include one or more processing cores, whichcan operate independently of each other. Additional co-processing units,such as a graphics processing unit 803, also can be present in thecomputer.

The system memory 804 can include volatile devices, such as dynamicrandom access memory (DRAM) or other random access memory devices. Thesystem memory 804 can also or alternatively include non-volatiledevices, such as a read-only memory or flash memory.

The computer 801 can include local non-volatile secondary storage 808such as a disk drive, solid state disk, or removable memory card. Thelocal storage 808 can include one or more removable and/or non-removablestorage units. The local storage 808 can be used to store an operatingsystem that initiates and manages various applications that execute onthe computer. The local storage 808 can also be used to store specialpurpose software configured to implement the components of theembodiments disclosed herein and that can be executed as one or moreapplications under the operating system.

The computer 801 can also include communication device(s) 812 throughwhich the computer communicates with other devices, such as one or moreremote computers 830, over wired and/or wireless computer networks 818.Communications device(s) 812 can include, for example, a networkinterface for communicating data over a wired computer network. Thecommunication device(s) 812 can include, for example, one or more radiotransmitters for communications over Wi-Fi, Bluetooth, and/or mobiletelephone networks.

The computer 801 can also access network storage 820 through thecomputer network 818. The network storage can include, for example, anetwork attached storage device located on a local network, orcloud-based storage hosted at one or more remote data centers. Theoperating system and/or special purpose software can alternatively bestored in the network storage 820.

The computer 801 can have various input device(s) 814 such as akeyboard, mouse, touchscreen, camera, microphone, accelerometer,thermometer, magnetometer, or any other sensor. Output device(s) 816such as a display, speakers, printer, eccentric rotating mass vibrationmotor can also be included.

The various storage 808, communication device(s) 812, output devices 816and input devices 814 can be integrated within a housing of thecomputer, or can be connected through various input/output interfacedevices on the computer, in which case the reference numbers 808, 812,814 and 816 can indicate either the interface for connection to a deviceor the device itself as the case may be.

Any of the foregoing aspects may be embodied in one or more instances asa computer system, as a process performed by such a computer system, asany individual component of such a computer system, or as an article ofmanufacture including computer storage in which computer programinstructions are stored and which, when processed by one or morecomputers, configure the one or more computers to provide such acomputer system or any individual component of such a computer system. Aserver, computer server, a host or a client device can each be embodiedas a computer or a computer system. A computer system may be practicedin distributed computing environments where operations are performed bymultiple computers that are linked through a communications network. Ina distributed computing environment, computer programs can be located inboth local and remote computer storage media.

Each component of a computer system such as described herein, and whichoperates on one or more computers, can be implemented using the one ormore processing units of the computer and one or more computer programsprocessed by the one or more processing units. A computer programincludes computer-executable instructions and/or computer-interpretedinstructions, such as program modules, which instructions are processedby one or more processing units in the computer. Generally, suchinstructions define routines, programs, objects, components, datastructures, and so on, that, when processed by a processing unit,instruct the processing unit to perform operations on data or configurethe processor or computer to implement various components or datastructures.

Components of the embodiments disclosed herein, which may be referred toas modules, engines, processes, functions or the like, can beimplemented in hardware, such as by using special purpose hardware logiccomponents, by configuring general purpose computing resources usingspecial purpose software, or by a combination of special purposehardware and configured general purpose computing resources.Illustrative types of hardware logic components that can be usedinclude, for example, Field-programmable Gate Arrays (FPGAs),Application-specific Integrated Circuits (ASICs), Application-specificStandard Products (ASSPs), System-on-a-chip systems (SOCs), and ComplexProgrammable Logic Devices (CPLDs).

Although the subject matter has been described in terms of certainembodiments, other embodiments, including embodiments which may or maynot provide various features and advantages set forth herein will beapparent to those of ordinary skill in the art in view of the foregoingdisclosure. The specific embodiments described above are disclosed asexamples only, and the scope of the patented subject matter is definedby the claims that follow.

In the claims, the term “based upon” shall include situations in which afactor is taken into account directly and/or indirectly, and possibly inconjunction with other factors.

1. A method performed by a computer system having a processor and amemory, the method comprising, the computer system: for each videosegment of a plurality of video segments: generating a content profilefor the video segment by performing an automated analysis of the videosegment, wherein the content profile comprises an inflection pointspecifying a time location within the video segment at which an eventidentified by the automated analysis occurs, and for each user of aplurality of users to which the video segment is presented: generatingan interaction profile for the user with respect to the video segmentbased on user input to a computer in association with a presentation ofthe video segment to the user, wherein the interaction profile comprisesan input time location within the video segment at which the userprovided input, and generating an interaction score based on theinteraction profile and a comparison between the inflection point andthe input time; for a target video segment of the plurality of videosegments, selecting a target plurality of users based on: the contentprofile for the target video segment, and for each of at least onesubject video segment selected from the plurality of video segments: thecontent profile for the subject video segment, and the plurality ofinteraction scores for users with respect to the subject video segment;and causing the target video segment to be presented to at least one ofthe selected target plurality of users.
 2. The method of claim 1,further comprising generating a user profile for each user of theplurality of users, wherein the selecting a target plurality of users isfurther based on the user profiles.
 3. The method of claim 2, whereinthe user profile for each user comprises at least one item selected froma group consisting of: location of the user, geolocation of the user,age of the user, gender of the user, and personality of the user.
 4. Themethod of claim 1, wherein the content profile further comprises aplurality of attribute fields each of which attribute fields specifies acharacteristic of the video segment determined based on the automatedanalysis, and wherein one of the attribute fields is configured to storea category assigned to the video segment by the automated analysis. 5.The method of claim 2, wherein selecting the target plurality of usersis further based on the plurality of interaction profiles for users withrespect to the subject video segment.
 6. The method of claim 5, whereinselecting the target plurality of users is further based on theplurality of interaction profiles for users with respect to the targetvideo segment.
 7. The method of claim 6, wherein the selecting a targetplurality of users comprises: determining an engagement likelihood scorebased on the content profile for the target video segment and furtherbased on, for each of the at least one subject video segment selectedfrom the plurality of video segments, the content profile for thesubject video segment and the plurality of interaction profiles forusers with respect to the subject video segment, wherein the targetplurality of users is selected based on the engagement likelihood score.8. The method of claim 7, wherein the user input for at least one usercomprises active user input selected from the group consisting of:keyboard input, pointer selection, button press, voice capture, andtouch screen selection.
 9. The method of claim 7, wherein the user inputfor at least one user comprises passive user input selected from thegroup consisting of: visibility of the presentation of the video segmenton a computer display, absence of user input interrupting thepresentation of the video segment, an indication that the presentationof the video segment continued to completion, and use of a camera todetermine whether the user is viewing the presentation of the videosegment.
 10. The method of claim 7, wherein the user input for at leastone user comprises visibility of the video segment on a computer displaydetermined based on whether another application window or scrolling of apage partly or fully obscures display of the video segment.
 11. Themethod of claim 1, wherein the interaction profile for at least one usercomprises one or more interaction fields.
 12. The method of claim 11,wherein one of the at least one interaction fields indicates whether thevideo segment was presented to the user in its entirety.
 13. The methodof claim 11, wherein one of the at least one interaction fieldsindicates how much of the video segment was presented to the user. 14.The method of claim 11, wherein one of the at least one interactionfields indicates a time location within the video segment at which timelocation the user selected a selectable link presented in associationwith the video segment.
 15. The method of claim 1, wherein theinteraction profile comprises an indication of intent by the user totake an action.
 16. The method of claim 15, wherein the indication ofintent by the user to take an action is obtained in response to a surveypresented to the user in association with the presentation of the videosegment to the user.
 17. The method of claim 1, wherein the selecting atarget plurality of users is performed using a trained machine learningsystem.
 18. The method of claim 17, wherein the trained machine learningsystem is trained using the content profiles for the plurality of videosegments and the interaction profiles for the plurality of users. 19.The method of claim 18, wherein the trained machine learning system is aneural network.
 20. The method of claim 18, wherein the trained machinelearning system is a Bayesian network.